# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE55976"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE55976"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE55976.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE55976.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE55976.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# The title and study design indicate gene expression profiling using cDNA microarrays
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# 2.1 Row identifiers in sample characteristics
trait_row = 0  # Disease condition is in row 0
age_row = None  # Age not available 
gender_row = None  # Gender not available

# 2.2 Conversion functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().lower()
    # Convert to binary for Essential Thrombocythemia
    if 'essential thrombocythemia' in value:
        return 1
    elif value in ['polycythemia vera (pv)', 'primary myelofibrosis jak2+', 
                  'primary myelofibrosis jak2-', 'chronic myelogenous leukemia',
                  'healthy donor']:
        return 0
    return None

def convert_age(value):
    # Not used since age data unavailable
    return None

def convert_gender(value):
    # Not used since gender data unavailable
    return None

# 3. Save initial filtering metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)

# 4. Extract clinical features
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(selected_clinical)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These identifiers (6590728, 6590730, etc.) appear to be probe IDs, not human gene symbols
# They look like Illumina probe IDs which need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# 1. Identify columns for gene identifier and gene symbol
# In gene_annotation, 'ID' column matches probe IDs in genetic_data
# 'GENE SYMBOL' column contains the corresponding gene symbols
prob_col = 'ID'
gene_col = 'GENE SYMBOL'

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")